AI in Healthcare and Medicine

Daniel Rückert

Over the last 20 years Professor Rückert has worked extensively in the area of biomedical image computing covering all aspects from image acquisition to image analysis and interpretation. His current research interests include:

  • Development of algorithms for image acquisition, image analysis and image interpretation – in particular in the areas of registration, reconstruction, tracking, segmentation and modelling.
  • Novel AI and machine learning algorithms for the extraction of clinically useful information from medical images – in particular for computer-aided detection, diagnosis and decision support.

Professor Rückert has a particularly strong interest in clinical translation: To facilitate translation of research to healthcare he has co-founded IXICO to commercialize the image analysis techniques developed in his research group. IXICO provides imaging analysis solutions and imaging biomarkers for clinical trials in the pharmaceutical industry and healthcare diagnostics. Since 2003, IXICO has rapidly grown and now employs 50 people. IXICO’s technologies have been used in more than 60 large-scale clinical trials involving more than 20.000 patients.

The mission of our group is to develop artificial intelligence (AI) and machine learning (ML) techniques for the analysis and interpretation of biomedical data. The group focuses on pursuing blue-sky research, including:

  • AI for the early detection, prediction and diagnosis of diseases
  • AI for personalized interventions and therapies
  • AI for the identification of new biomarkers and targets for therapy
  • Safe, robust and interpretable AI approaches
  • Privacy-aware AI approaches

We have particularly strong interest in the application of imaging and computing technology to improve the understanding brain development (in-utero and ex-utero), to improve the diagnosis and stratification of patients with dementia, stroke and traumatic brain injury as well as for the comprehensive diagnosis and management of patients with cardiovascular disease and cancer.

  • Fellow, British Computer Society (2010)
  • Fellow, MICCAI Society (2014)
  • Fellow, Royal Academy of Engineering (2015)
  • Fellow, IEEE (2015)
  • Fellow, Academy of Medical Sciences (2019)
  • Fellow, International Academy of Medical & Biological Engineering (2020)
  • Alexander von Humboldt Professorship for Artificial Intelligence (2020)
  • ERC Synergy Grant (2013), ERC Advanced Grant (2020)

K. Kamnitsas, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert and B. Glocker. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis 36: 61-78, 2017.

J. Schlemper, J. Caballero, J. V. Hajnal, A. N. Price and D. Rueckert. A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction. IEEE Transactions on Medical Imaging, 37(2): 491-503, 2018.

G. A. Bello, T. J. W. Dawes, J. Duan, C. Biffi. A. de Marvao, L. S. G. E. Howard, J. S. R. Gibbs, M. R. Wilkins, S. A. Cook, D. Rueckert and D. P. O'Regan. Deep learning cardiac motion analysis for human survival prediction. Nature Machine Intelligence. 1:95-104, 2019.

D. Rueckert and J. A. Schnabel. Model-Based and Data-Driven Strategies in Medical Image Computing. Proceedings of the IEEE 108(1): 110-124, 2020.

G. A. Kaissis, M. R. Makowski, D. Rueckert and R. F. Braren. Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence 2: 305–311, 2020.